Transcript slides - Data Sciences and Knowledge Discovery Laboratory

DDDM 2008: The 2nd International
Workshop on Domain Driven Data Mining
Philip S. Yu, Yanchang Zhao,
Graham Williams, Carlos Soares
Outline
 DDDM:
Domain Driven Data
Mining
 DDDM 2007
 DDDM 2008
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Background
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In the last decade, data mining has emerged
as one of most vivacious areas in information
technology.
 Although many algorithms and techniques for
data mining have been proposed, it still
remains an open problem to successfully
apply them to discover actionable knowledge
in real-life applications in various domains.
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DDDM
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The International Workshop on Domain Driven Data
Mining (DDDM)
Aims:
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To provide a premier forum for sharing findings,
knowledge, insight, experience and lessons in tackling
potential challenges in discovering actionable
knowledge from complex domain problems,
To promote the interaction of and bridge the gap
between data mining research and business
expectations, and
To drive a paradigm shift from traditional data-centered
hidden pattern mining to domain-driven actionable
knowledge discovery.
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Objectives
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To design next-generation data mining methodology for
actionable knowledge discovery and identify how KDD
techniques can better contribute to critical domain
problems in theory and practice;
 To devise domain-driven data mining techniques to
strengthen business intelligence in complex enterprise
applications;
 To present the applications of domain-driven data
mining and demonstrate how KDD can be effectively
deployed to solve complex practical problems; and
 To identify challenges and future directions for data
mining research and development in the dialogue
between academia and industry.
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DDDM 2007
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San Jose, California, USA, on 12th August 2007
In conjunction with ACM SIGKDD'07
Website: http://datamining.it.uts.edu.au/dddm/
8 papers accepted from 5 countries
Organizing Committee
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General Chair
Philips Yu, IBM T.J. Watson Research Center, USA
Workshop Chairs
Chengqi Zhang, University of Technology, Sydney, Australia
Graham Williams, Australian Taxation Office, Australia
Longbing Cao, University of Technology, Sydney, Australia
Organizing Chair
Yanchang Zhao, University of Technology, Sydney, Australia
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DDDM 2008
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Pisa, Italy, on December 15, 2008
In conjunction with IEEE ICDM'08
Website:
http://datamining.it.uts.edu.au/dddm08/
39 submissions from 12 countries (including
papers forwarded from main conference)
10 papers accepted, with an acceptance rate
of 26%
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Organizing Committee
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General Chair
Philip S. Yu
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University of Illinois at Chicago, USA
Program Chairs
Yanchang Zhao University of Technology, Sydney,
Australia
Graham Williams Australian Taxation Office, Australia
Carlos Soares
University of Porto, Portugal
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Host
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Data Sciences & Knowledge Discovery Research
Lab
http://datamining.it.uts.edu.au
Centre for Quantum Computation and Intelligent
Systems
http://www.qcis.uts.edu.au
University of Technology, Sydney, Australia
http://www.uts.edu.au
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Program Committee
Ronnie Alves
Elena Baralis
David Bell
Petr Berka
Jean-Francois Boulicaut
Longbing Cao
Peter Christen
Paulo Cortez
Guozhu Dong
Warwick Graco
Joshua Zhexue Huang
Alexandros Kalousis
Walter Kosters
Christopher Leckie
Chunhung Li
Xue Li
Tsau Young Lin
Universidade do Minho, Portugal
Politecnico di Torino, Italy
Queen's University Belfast, UK
University of Economics of Prague, Czech Republic
INSA Lyon, France
University of Technology, Sydney, Australia
The Australian National University, Australia
University of Minho, Portugal
Wright State University, USA
Australian Taxation Office, Australia
The University of Hong Kong, Hong Kong
The Universtity of Geneva, Switzerland
Leiden University, The Netherlands
The University of Melbourne, Australia
Hong Kong Baptist University, Hong Kong
The University of Queensland, Australia
San Jose State University, USA
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Program Committee (cont.)
Donato Malerba
Engelbert Mephu Nguifo
Ngoc Thanh Nguyen
Arlindo Oliveira
Alexandre Plastino
Kulathur S. Rajasethupathy
Yidong Shen
Dan Simovici
Wei Wang
Jeffrey Xu Yu
Carlo Zaniolo
Justin Zhan
Chengqi Zhang
Huaifeng Zhang
Mengjie Zhang
Shichao Zhang
Zhi-Hua Zhou
University of Bari, Italy
Universite d'Artois, France
Wroclaw University of Technology, Poland
IST/INESC-ID, Portugal
Universidade Federal Fluminense, Brazil
State University of New York, USA
Chinese Academy of Sciences, China
University of Massachusetts at Boston, USA
Fudan University, China
The Chinese University of Hong Kong, Hong Kong
University of California, Los Angeles, USA
Carnegie Mellon University, USA
University of Technology, Sydney, Australia
University of Technology, Sydney, Australia
Victoria University of Wellington, New Zealand
Guangxi Normal University, China
Nanjing University, China
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TKDE Special Issue on DDDM
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IEEE Transactions on Knowledge and Data
Engineering Special Issue on Domain Driven
Data Mining
Guest Editors: Chengqi Zhang, Philip S. Yu,
David Bell
Submission deadline: March 31, 2009
http://datamining.it.uts.edu.au/group/cfp/cfpDDDM.doc
http://www.computer.org/portal/cms_docs_tran
sactions/transactions/tkde/CFP/cfp_tkde_dom
ain-driven.pdf
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2:00 pm Opening address
Program
2:10 pm Keynote speech
Domain Driven Data Mining (D3M)
by Longbing Cao, University of Technology, Sydney, Australia
2:40 pm Session I
S2211: Food Sales Prediction: "If Only It Knew What We Know“, by Patrick Meulstee and Mykola
Pechenizkiy
S2205: Parameter Tuning for Differential Mining of String Patterns, by Jeremy Besson,
Christophe Rigotti, Ieva Mitasiunaite, and Jean-Francois Boulicaut
S2202: Discovering Implicit Redundancies in Network Communications for Detecting
Inconsistent Values, by Bogdan Nassu, Takashi Nanya, and Hiroshi Nakamura
S2208: Identification of Causal Variables for Building Energy Fault Detection by Semi-supervised
LDA and Decision Boundary Analysis, by Keigo Yoshida, Minoru Inui, Takehisa Yairi, Kazuo
Machida, Masaki Shioya, and Yoshio Masukawa
4:00 pm Coffee Break
4:15pm
Session II
S2206: Actionable Knowledge Discovery for Threats Intelligence Support using a MultiDimensional Data Mining Methodology, by Olivier Thonnard and Marc Dacier
DM422: One-class Classification of Text Streams with Concept Drift, by Xue Li and Yang Zhang
DM830: Post-Processing of Discovered Association Rules using Ontologies, by Claudia
Marinica, Fabrice Guillet, and Henri Briand
S2212: Behavior Informatics and Analytics: A New and Promising Area, by Longbing Cao
DM424: TransRank: A Novel Algorithm for Transfer of Rank Learning, by Depin Chen, Jun Yan,
Gang Wang, and Weiguo Fan
DM698: Scoring Models for Insurance Risk Sharing Pool Optimization, by Nicolas Chapados,
Charles Dugas, Pascal Vincent, and Rejean Ducharme